Polyps are abnormal growths of tissue projecting from a mucous membrane, and are commonly found in the colon, stomach, nose, bladder and uterus. Polyps can be flat, pedunculated, i.e. mushroom shaped, or sessile. Colon polyps, in particular, are a concern as benign colon polyps have the potential to transform with time into colon cancer. It is thought that flat polyps, or flat lesions, may lead to more aggressive cancer and may progress to cancers more rapidly than is typically the case with pedunculated or sessile polyps.
Computed Tomography Colonography (CTC), or Virtual Colonoscopy (VC), has become a particularly popular method in the identification of pre-cancerous polyps on the colon wall. The screening technique is non-invasive and is based on a high resolution 3-D X-ray scan of the subject's abdomen. In 2001, the feasibility of automated polyp detection in CTC was discussed by Summers et al. (Radiology 219, 51-59).
In order to improve the efficiency and accuracy associated with locating pre-cancerous polyps in a CTC scan, various Computer Aided Detection/Computer Assisted Diagnosis (CAD) techniques have been developed to assist the examining radiologist in assessing the results of a CTC data scan. CAD-CTC systems aim to provide the radiologist with locations for potential regions of particular interest within the very large datasets generated by CTC. This is essentially an assisted tool for radiologists who, upon examining the data provided by the method, can make an informed decision as to whether to return to a specific region in the dataset and examine the point of a potential polyp. To date, the focus of such CTC-CAD systems has been in the detection of raised (pedunculated and sessile) polyps protruding significantly from the colonic surface.
Some of the representative CTC-CAD polyp detection algorithms are disclosed below. Görtürk and co-workers (IEEE Transactions on Medical Imaging 2001, 20(12), 1251-1260) describe a statistical approach utilising training data and support vector machines to distinguish the differentiating characteristics of polyps and healthy tissue and make classifications based on this data. Yoshida and Nappi (IEEE Transactions on Medical Imaging 2001, 20(12), 1261-1274) employ hysteresis thresholding to isolated candidate polyps based on standard 3D geometric features. The candidates are further subjected to fuzzy clustering and discriminant analysis to reduce the number of false positives. The efficiency of a post-processing algorithm premised on edge displacement field-based classification was evaluated by Acar and co-workers as a means of improving polyp detection and decreasing the number of false positives found (IEEE Transactions on Medical Imaging 2004, 21(12), 1461-1467).
In 2002 Kiss et al. developed a dual surface normal and sphere fitting method for polyps detection (European Radiology 2002, 12(1), 77-81). A subsequent disclosure of a modified surface normal overlap method for the detection of colonic polyps and lung nodules was described by Paik et al. (IEEE Transactions on Medical Imaging 2004, 23(6), 661-675). The surface normal methods exploit the fact that normals on the colon surface intersect with neighbouring normals depending on the curvature features of the colon. The mantra of these surface normal methods is that polyps show a high incidence of surface normal intersection owing to their spherical nature.
A number of issued patents discuss the processes involved in segmenting virtual endoscopy images/digital medical images and detecting abnormal lesions and distortions in these images (see U.S. Pat. Nos. 5,133,020, 6,078,680, 6,366,800, 6,556,696, and 6,909,913). The subject matter of these patents is not directed to the development of a method for detecting flat polyps, which are particularly difficult to detect through existing CAD techniques.
US Patent Application Nos. 2005/0078858, 2005/0149286, 2005/0152588 all disclose methods for the detection of abnormal structures and growths in medical images. These three patent applications also place specific emphasis on the detection of polyps in the colon.
Existing CAD techniques primed for detecting raised polyps can also detect some flat polyps, however the sensitivity is low. The anisotropic characteristics of flat polyps make their detection more difficult. Features utilised in current methods for the detection of polyps are not well suited to identifying flat polyps. Manual examination of a CTC scan is an option, but again the detection rate of flat polyps is quite low. Thus, there is a necessity for a method that can robustly identify potential flat polyps whilst minimising the occurrence of false positives.